Date
Publisher
arXiv
This study investigates the strategic and epistemically responsible
integration of AI-powered chatbots into physics teacher education by employing
a TPACK-guided SWOT framework across three structured learning activities.
Conducted within a university-level capstone course on innovative tools for
physics instruction, the activities targeted key intersections of
technological, pedagogical, and content knowledge (TPACK) through
chatbot-assisted tasks: simplifying abstract physics concepts, constructing
symbolic concept maps, and designing instructional scenarios. Drawing on
participant reflections, classroom artifacts, and iterative feedback, the
results highlight internal strengths such as enhanced information-seeking
behavior, scaffolded pedagogical planning, and support for symbolic reasoning.
At the same time, internal weaknesses emerged, including domain-specific
inaccuracies, symbolic limitations (e.g., LaTeX misrendering), and risks of
overreliance on AI outputs. External opportunities were found in promoting
inclusive education, multilingual engagement, and expanded zones of proximal
development (ZPD), while external threats included prompt injection risks,
institutional access gaps, and cybersecurity vulnerabilities. By extending
existing TPACK-based models with constructs such as AI literacy,
prompt-crafting competence, and epistemic verification protocols, this research
offers a theoretically grounded and practically actionable roadmap for
embedding AI in STEM teacher preparation. The findings affirm that, when
critically scaffolded, AI chatbots can support metacognitive reflection,
ethical reasoning, and instructional innovation in physics education if
implementation is paired with digital fluency training and institutional
support.
Study at a glance
- Question: How can AI chatbots be responsibly and effectively integrated into physics teacher education?
- Setting and data: University capstone course on physics instruction tools; participant reflections, classroom artifacts, and iterative feedback.
- Method: Qualitative analysis of participant reflections and coursework using a strengths, weaknesses, opportunities, and threats framework (TPACK-guided SWOT analysis).
Why it matters
Integrating AI-powered chatbots into physics teacher preparation requires strategic and epistemically responsible approaches. Existing TPACK models must be extended to address pedagogical, epistemic, and cybersecurity dimensions, including AI literacy and epistemic verification protocols.
Key findings
- In a university physics teacher preparation course, using AI chatbots supported future teachers with lesson planning, symbolic reasoning, and finding information.
- Chatbots produced physics-specific inaccuracies and formatting errors with symbols, while creating risks that future teachers would overly rely on AI outputs.
- Chatbot use presented cybersecurity vulnerabilities, prompt injection risks, and institutional access gaps, alongside opportunities for inclusive, multilingual learning.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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Why use AI?
Study design
